Predictive maintenance techniques powered by artificial intelligence (AI) are helping enterprises across industries, such as manufacturing, to find patterns that can avoid machine failures.
Unlike AI, traditional business intelligence systems are not designed to handle huge volumes of industrial Internet of things (IIoT) data, splurging the massive economic benefits to enterprises, says data analytics company, GlobalData.
Venkata Naveen, disruptive tech analyst at GlobalData, comments: “Predictive maintenance is a key cost-saving digital strategy for any enterprise. AI-powered predictive maintenance can help enterprises to save money and time on maintenance, machine downtime while extending the life of their heavy equipment.”
The Innovation Explorer Database of GlobalData’s Disruptor Intelligence Center reveals how predictive maintenance is increasingly becoming crucial across the value chains of various industries such as automotive, manufacturing, oil and gas, mining, power and aerospace.
Mitsubishi Electric has developed AI-based diagnostic technology that harnesses machine learning algorithms to analyse sensor data of machines and generate a model of the machine’s transition between different operational states. The model is then used to set optimal conditions for detecting abnormalities of a machine during each operational state, enabling operators to gauge signs of machinery failure before actual breakdowns.
American Family Insurance has collaborated with insurtech Neos to offer a smart home insurance product to US customers. The product consists of a smart water leak detector and cameras along with a mobile app connecting them. It leverages AI algorithms to analyse an individual’s water usage over a period of time establishing a pattern. Any changes in the water usage patterns can help Neos predict issues with the pipelines. The Neos app alerts users on potential issues such as dripping taps and hidden leaks on pipes along with a live camera feed. Neos instantly connects customers with repair services through its platform.
Agnico Eagle Mines has partnered with Montreal’s Newtrax Technologies to predict mobile equipment maintenance issues before they happen using AI algorithms to the IoT sensors data. Newtrax helped Agnico Eagle to analyse an engine that has shown signs of a potential issue, which helped the mining company to save US$63,610 in repairs and replacement of the engine.
Instead of developing a digital predictive maintenance system from scratch, enterprises are partnering with startups in the space to deploy their predictive AI solutions off-the-shelf. Some of the popular interesting technology providers include C3.ai, Uptake Technologies, Maana, Sight Machines, Predictive-Sigma and Presenso.
Naveen concludes: “One of the critical challenges for predictive maintenance is streamlining the flow of data from machines to a central system with a low level of latency and high security, which can be overcome given the advancements in 5G connectivity and cybersecurity. Despite the stumbling blocks, predictive maintenance is a vital part of an enterprise’s digital transformation strategy.”